Super Micro Computer, Inc. (SMCI), a global leader in compute, storage, networking technologies and green computing today announced the general availability of its SuperServer solutions optimized for NVIDIA Tesla P100 accelerators with the new Pascal GPU architecture. Supermicro's innovative and GPU optimized single root complex PCI-E design is proven to dramatically improve GPU peer-to-peer communication efficiency over QPI and PCI-E links, with up to 21% higher QPI throughput and 60% lower latency compared to previous generation products. "Our high-performance computing solutions enable deep learning, engineering, and scientific fields to scale out their compute clusters to accelerate their most demanding workloads and achieve fastest time-to-results with maximum performance-per-watt, per-square-foot, and per-dollar," said Charles Liang, President and CEO of Supermicro. "With our latest innovations incorporating the new NVIDIA P100 GPUs, our customers can accelerate their applications and innovations to solve the most complex real world problems." "Supermicro's new high-density servers are optimized to fully leverage the new NVIDIA Tesla P100 accelerators to provide enterprise and HPC customers with an entirely new level of computing horsepower," said Ian Buck, General Manager of the Accelerated Computing Group at NVIDIA.

Time series machine learning techniques show great promise for the analysis of health care wearable data. As our busy lifestyles render continuous monitoring more and more essential, the need to analyze data to find correlations between these data streams becomes even more important, because they can provide important cues to people. These cues could be as simple as reminding a person to take a walk or move around, which is already being done by a lot of wearables available today, such as Fitbit, Garmin, Nike, etc. However, along with monitoring the current state of an individual, these popular devices are not able to perform the complex predictions that correlate the captured information to make sense at a higher level or provide causal relationships between the data. My research aims to develop advanced algorithms for analyzing time series data for estimation and prediction of physiological parameters (such as heart rate or respiration rate using kinematic and physiological data).

For decades, science fiction authors, futurists, and movie makers alike have been predicting the amazing (and sometimes catastrophic) changes that will arise with the advent of widespread artificial intelligence. So far, AI hasn't made any such crazy waves, and in many ways has quietly become ubiquitous in numerous aspects of our daily lives. From the intelligent sensors that help us take perfect pictures, to the automatic parking features in cars, to the sometimes frustrating personal assistants in smartphones, artificial intelligence of one kind of another is all around us, all the time. While we've yet to create self-aware robots like those that pepper popular movies like 2001: A Space Odyssey and Star Wars, we have made smart and often significant use of AI technology in a wide range of applications that, while not as mind-blowing as androids, still change our day-to-day lives. One place where artificial intelligence is poised to make big changes (and in some cases already is) is in education.

International firm Reed Smith expects to make greater use of artificial intelligence technology for transactional work following a successful pilot in its London office. After using AI technology for a real estate matter, chief knowledge officer Lucy Dillon (pictured) said the firm will definitely be using it again. 'I think we will be using [the technology] more widely as it lends itself to any transaction where you are reviewing large reams of documents,' she added. The firm tested a'cognitive computing platform' developed by software provider RAVN Systems. The software was used to read, interpret and extract key provisions from a client's leases. It then produced a review identifying higher-risk leases that required further inspection.

In this observation, we ranked nearly 1,750 articles posted in August 2016 about machine learning, deep learning and AI. Mybridge AI evaluates the quality of content and ranks the best articles for professionals. This list is competitive and carefully includes quality content for you to read. You may find this condensed list useful in learning and working more productively in the field of machine learning.

Reuters – The new Apple iPhone has something missing: the small socket millions of us have used for years to plug in headphones. While some fans opposed the widely anticipated move – one online petition urging Apple to keep the headphone jack drew more than 300,000 signatures – equipment suppliers and experts heralded a change in how users will interact with their devices. Axing the jack, they say, paves the way for discreet, bean-sized earbuds that can simultaneously translate, filter out unwanted noise or let us control other devices by voice – and drive up the value of the so-called'hearables' market to 16 billion within five years. It's the vision of the futuristic 2013 movie "Her", where a human has a love affair with a disembodied voice in his ear. But some who follow the industry say it's closer than many think, noting improvements in wireless technologies, materials, artificial intelligence and battery life.

The first international beauty contest judged by "machines" was supposed to use objective factors such as facial symmetry and wrinkles to identify the most attractive contestants. After Beauty.AI launched this year, roughly 6,000 people from more than 100 countries submitted photos in the hopes that artificial intelligence, supported by complex algorithms, would determine that their faces most closely resembled "human beauty". But when the results came in, the creators were dismayed to see that there was a glaring factor linking the winners: the robots did not like people with dark skin. Out of 44 winners, nearly all were white, a handful were Asian, and only one had dark skin. That's despite the fact that, although the majority of contestants were white, many people of color submitted photos, including large groups from India and Africa.

Israel has deployed autonomous military vehicles to patrol the border of the Palestinian Authority. These vehicles are apparently unarmed, so far, and groups of them are controlled by a remotely placed soldier, so they are not quite up to the level of a Robocop, which I suppose is some kind of relief. There are no fully robotic warriors out there, yet. Something along the lines of, that which does not kill us now, might still kill us later if we aren't ready. The short version of all this is that as AI research engages more and more with the real world, things may take some dangerous turns.

The year 2015 might be seen as the year that "artificial intelligence risk" or "artificial intelligence danger" went mainstream (or close to it). With the founding of Elon Musk's Open AI and The Leverhulme Centre for the Future of Intelligence, the increased attention on the Future of Life Institute and Oxford's Future of Humanity Institute, and a flurry of attention around celebrity comments around AI dangers (including the now well-known statements of Bill Gates and Elon Musk), it's safe to say that AI risk has embedded itself as a topic of pop-culture discourse – even if it's not a very serious one at present. Recently, we interviewed and reached out to a total of over 30 artificial intelligence researchers (all except one hold a PhD) and asked them about the AI risks that they believe to be the most pressing in the next 20 years, as well as the next 100 years. Below you can see a list of all of our respondents; clicking on a respondent will bring up their answer to the 20-year risk question. Interestingly enough, automation and economic impact topped the list, coinciding with the massive amount of media attention on autonomous vehicles and improved robotic manufacturing, among other industries.

In the age of machine learning, what should managers know -- and what must non-tech companies do to stay ahead? This article is part of an MIT SMR initiative exploring how technology is reshaping the practice of management. For data scientists and machine-learning experts, March 2016 was a momentous month. AlphaGo, a computer program developed by Google, beat world champion Lee Sedol at the ancient Chinese board game Go by a score of 4 to 1. In contrast to chess, where players might make about 40 moves per game, games of Go may have 200 moves.1 Whereas IBM's Deep Blue was used to defeat chess grand master Garry Kasparov in 1997, computer scientists can't calculate all the moves required to win at Go. Instead, Google had to create another kind of machine algorithm that could approximate humanlike qualities, playing the game by intuition and feel.